| from __future__ import annotations |
|
|
| from typing import Iterable, TYPE_CHECKING |
|
|
| import torch |
|
|
| if TYPE_CHECKING: |
| from torch import Tensor |
|
|
| from .base import ModelBase, TextModel, gguf, logger |
|
|
|
|
| @ModelBase.register("GPT2LMHeadModel") |
| class GPT2Model(TextModel): |
| model_arch = gguf.MODEL_ARCH.GPT2 |
|
|
| def set_gguf_parameters(self): |
| self.gguf_writer.add_block_count(self.block_count) |
| self.gguf_writer.add_context_length(self.hparams["n_ctx"]) |
| self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) |
| self.gguf_writer.add_feed_forward_length(4 * self.hparams["n_embd"]) |
| self.gguf_writer.add_head_count(self.hparams["n_head"]) |
| self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) |
| self.gguf_writer.add_file_type(self.ftype) |
|
|
| def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: |
| |
| if name.endswith((".attn.bias", ".attn.masked_bias")): |
| yield from super().modify_tensors(data_torch, name, bid) |
| return |
|
|
| if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): |
| data_torch = data_torch.transpose(1, 0) |
|
|
| new_name = self.map_tensor_name(name) |
|
|
| yield from super().modify_tensors(data_torch, new_name, bid) |
|
|
|
|
| @ModelBase.register("RuGPT3XLForCausalLM") |
| class RuGPT3XLModel(TextModel): |
| model_arch = gguf.MODEL_ARCH.GPT2 |
|
|
| _qkv_parts: list[dict[str, Tensor]] | None = None |
|
|
| def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: |
| |
| if ".self_attn.q_proj." in name or ".self_attn.k_proj." in name or ".self_attn.v_proj." in name: |
| suffix = "weight" if name.endswith(".weight") else "bias" |
| part = "q" if ".q_proj." in name else ("k" if ".k_proj." in name else "v") |
| key = f"{part}.{suffix}" |
|
|
| assert bid is not None |
| if self._qkv_parts is None: |
| self._qkv_parts = [{} for _ in range(self.block_count)] |
| self._qkv_parts[bid][key] = data_torch |
|
|
| q_key, k_key, v_key = f"q.{suffix}", f"k.{suffix}", f"v.{suffix}" |
| if all(k in self._qkv_parts[bid] for k in [q_key, k_key, v_key]): |
| q = self._qkv_parts[bid].pop(q_key) |
| k = self._qkv_parts[bid].pop(k_key) |
| v = self._qkv_parts[bid].pop(v_key) |
| data_torch = torch.cat([q, k, v], dim=0) |
| name = self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_QKV, bid, f".{suffix}") |
| logger.debug(f"Fused Q/K/V {suffix} for layer {bid} -> {name}") |
| else: |
| return |
|
|
| yield from super().modify_tensors(data_torch, name, bid) |
|
|
| def prepare_tensors(self): |
| super().prepare_tensors() |
|
|
| if self._qkv_parts is not None: |
| |
| parts = [f"({i}){k}" for i, d in enumerate(self._qkv_parts) for k in d.keys()] |
| if len(parts) > 0: |
| raise ValueError(f"Unprocessed Q/K/V parts: {parts}") |
|
|
